Work

Putting MARS into space. Non‐linearities and spatial effects in hedonic models

Year: 2023

Type: article

Abstract: Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non-parametric machine learning algorithm that automatizes the selection of non-linear terms in regression models. In this study, we propose using MARS in a spatial regression framework to account for potential non-linearities and spatial effects in spatial regression models. Using a relatively large data set of 17,000 dwellings in St. Petersburg (Russia), we examine how this algorithm works. The empirical evidence shows that most explanatory variables in the spatial regression model — including the spatial lag of the dependent variable — have a non-linear impact on the asking prices of dwellings.

Source: Papers of the Regional Science Association

DOI: 10.1111/pirs.12738

Authors: Fernando López, Konstatin Kholodilin

Institutions: Universidad Politécnica de Cartagena, German Institute for Economic Research

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,660

Funder: Ministerio de Ciencia e Innovación